code: def setUpClass(cls):
      ray.init()
  
      # When dealing with two policies in these tests, simply alternate between the 2
      # policies to make sure we have data for inference for both policies for each
      # step.
      class AlternatePolicyMapper:
          def __init__(self):
              self.policies = ["one", "two"]
              self.next = 0
  
          def map(self):
              p = self.policies[self.next]
              self.next = 1 - self.next
              return p
  
      cls.mapper = AlternatePolicyMapper()
  
imports: import unittest
  import numpy
  import ray
  from ray.rllib.algorithms.callbacks import DefaultCallbacks
  from ray.rllib.algorithms.ppo import PPO, PPOConfig
  from ray.rllib.connectors.connector import ActionConnector, ConnectorContext
  from ray.rllib.evaluation.metrics import RolloutMetrics
  from ray.rllib.examples.env.debug_counter_env import DebugCounterEnv
  from ray.rllib.examples.env.multi_agent import BasicMultiAgent, GuessTheNumberGame
  from ray.rllib.examples.policy.random_policy import RandomPolicy
  from ray.rllib.policy.policy import PolicySpec
  from ray.tune import register_env
  from ray.rllib.policy.sample_batch import convert_ma_batch_to_sample_batch
  from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
  from ray.rllib.core.rl_module.marl_module import MultiAgentRLModuleSpec
  from ray.rllib.examples.rl_module.random_rl_module import RandomRLModule
  from ray.rllib.utils.test_utils import check
label_desc: Use image node_id 1 for calling the TestEnvRunnerV2 obj's underlying member method code with example usage: obj.setUpClass(cls) and returns: p